A soft sensor was developed for quality estimation of diesel fuel as the crude distillation unit product. Due to the stringent fuel quality standards and the growing need to produce various gradations of quality with different feeds, laboratory testing and quality control of the products have become a necessity. On the basis of available continuous temperature measurements and flows of appropriate process streams, software sensors for estimating the cold filter plugging point of diesel fuel were developed. Nonlinear software sensor models were built with the best results achieved by using multilayer neural networks. Statistical data analysis was carried out and the data were critically evaluated. The results showed that soft sensors can be applied for refinery product quality estimation of diesel fuel as an alternative to laboratory testing. So, it becomes possible to continuously estimate fuel quality and to apply methods of inferential control for plant operation optimization.